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Structural Vector Autoregressive with Sign restrictions
- Program file to run a VAR model with p-lags % Author: Jefferson Martinez te amo % This code has been created only for academic and teaching purposes. Feel % free to use it, but do not forget to acknowledge the work. % I
Clark (1989) model for estimating unobservable components model
- The code allows to estimate the Clarck model by maximum likelihood. It is assumed that the series has 2 unobservable components: a trend and a cycle. In the case of the trend, an autoregressive process of order 2 is assu
Autocorrelation Function and Partial Autocorrelation Function
- The code allows calculating the autocorrelation function and the partial autocorrelation function of a time series. The algorithm is based on the Schwartz selection criteria, also called the BIC criterion. Also, the code
Newton-Rapshon Optimization
- The following code allows you to optimize non-linear functions using the algorithm of newton raphson. Analytical derivatives are used, the gradient and the Hessian matrix of the function to find maxima and minima. Two ex
Estimation codes of Econometric Modelling with Time Series: Specification, Estimation and Testing
- The present codes allow for estimation of multiple model in time series analysis. Among the principal models are ARMA, Vector Error Correction and Vector Autoregressive. The codes are written in Matlab.
Kalman filter: Multivariate and Univariate
- This code allows to calculate the recursive kalman filter and to estimate kalman filter. The files are: 1) Calculate recursive univariate kalman filter 2) Calculate recurisve multivariate kalman filter 3) Estimate kalman
Markov-Switching
- This code performs the univariate analysis of Markov-Switching model. The model shows step by step the implementation of Markov Chains to estimate multiple states and asymmetries in time series. The example is performed
Autoregressive Conditional Heterocedasticity
- This code performs multiple ARCH models in order to model the second moment of time series. It is implemented in Matlab and it is used to model variance of returns in S&P 500 and returns of Latin American countries.